import pandas as pd
import numpy as np
示例 股票数据离散化
stock=pd.read_csv("/home/python/练习/stock_day/stock_day.csv")
stock.head()
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|
open |
high |
close |
low |
volume |
price_change |
p_change |
ma5 |
ma10 |
ma20 |
v_ma5 |
v_ma10 |
v_ma20 |
turnover |
2018-02-27 |
23.53 |
25.88 |
24.16 |
23.53 |
95578.03 |
0.63 |
2.68 |
22.942 |
22.142 |
22.875 |
53782.64 |
46738.65 |
55576.11 |
2.39 |
2018-02-26 |
22.80 |
23.78 |
23.53 |
22.80 |
60985.11 |
0.69 |
3.02 |
22.406 |
21.955 |
22.942 |
40827.52 |
42736.34 |
56007.50 |
1.53 |
2018-02-23 |
22.88 |
23.37 |
22.82 |
22.71 |
52914.01 |
0.54 |
2.42 |
21.938 |
21.929 |
23.022 |
35119.58 |
41871.97 |
56372.85 |
1.32 |
2018-02-22 |
22.25 |
22.76 |
22.28 |
22.02 |
36105.01 |
0.36 |
1.64 |
21.446 |
21.909 |
23.137 |
35397.58 |
39904.78 |
60149.60 |
0.90 |
2018-02-14 |
21.49 |
21.99 |
21.92 |
21.48 |
23331.04 |
0.44 |
2.05 |
21.366 |
21.923 |
23.253 |
33590.21 |
42935.74 |
61716.11 |
0.58 |
qcut=pd.qcut(stock["p_change"], 10)
qcut.value_counts
<bound method IndexOpsMixin.value_counts of 2018-02-27 (1.738, 2.938]
2018-02-26 (2.938, 5.27]
2018-02-23 (1.738, 2.938]
2018-02-22 (0.94, 1.738]
2018-02-14 (1.738, 2.938]
2018-02-13 (0.94, 1.738]
2018-02-12 (2.938, 5.27]
2018-02-09 (-10.030999999999999, -4.836]
2018-02-08 (0.26, 0.94]
2018-02-07 (-2.444, -1.352]
2018-02-06 (-4.836, -2.444]
2018-02-05 (1.738, 2.938]
2018-02-02 (0.26, 0.94]
2018-02-01 (-10.030999999999999, -4.836]
2018-01-31 (-0.462, 0.26]
2018-01-30 (-0.462, 0.26]
2018-01-29 (-4.836, -2.444]
2018-01-26 (0.26, 0.94]
2018-01-25 (-4.836, -2.444]
2018-01-24 (-1.352, -0.462]
2018-01-23 (0.94, 1.738]
2018-01-22 (-0.462, 0.26]
2018-01-19 (1.738, 2.938]
2018-01-18 (-0.462, 0.26]
2018-01-17 (0.26, 0.94]
2018-01-16 (2.938, 5.27]
2018-01-15 (-4.836, -2.444]
2018-01-12 (1.738, 2.938]
2018-01-11 (-1.352, -0.462]
2018-01-10 (-1.352, -0.462]
...
2015-04-13 (5.27, 10.03]
2015-04-10 (-1.352, -0.462]
2015-04-09 (5.27, 10.03]
2015-04-08 (2.938, 5.27]
2015-04-07 (5.27, 10.03]
2015-04-03 (0.94, 1.738]
2015-04-02 (0.26, 0.94]
2015-04-01 (0.26, 0.94]
2015-03-31 (-2.444, -1.352]
2015-03-30 (2.938, 5.27]
2015-03-27 (5.27, 10.03]
2015-03-26 (-2.444, -1.352]
2015-03-25 (-2.444, -1.352]
2015-03-24 (1.738, 2.938]
2015-03-23 (-0.462, 0.26]
2015-03-20 (-0.462, 0.26]
2015-03-19 (0.26, 0.94]
2015-03-18 (0.26, 0.94]
2015-03-17 (1.738, 2.938]
2015-03-16 (1.738, 2.938]
2015-03-13 (1.738, 2.938]
2015-03-12 (-1.352, -0.462]
2015-03-11 (-2.444, -1.352]
2015-03-10 (1.738, 2.938]
2015-03-09 (-0.462, 0.26]
2015-03-06 (5.27, 10.03]
2015-03-05 (1.738, 2.938]
2015-03-04 (0.94, 1.738]
2015-03-03 (0.94, 1.738]
2015-03-02 (1.738, 2.938]
Name: p_change, Length: 643, dtype: category
Categories (10, interval[float64]): [(-10.030999999999999, -4.836] < (-4.836, -2.444] < (-2.444, -1.352] < (-1.352, -0.462] ... (0.94, 1.738] < (1.738, 2.938] < (2.938, 5.27] < (5.27, 10.03]]>
pd.get_dummies(qcut,prefix="rise")
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|
rise_(-10.030999999999999, -4.836] |
rise_(-4.836, -2.444] |
rise_(-2.444, -1.352] |
rise_(-1.352, -0.462] |
rise_(-0.462, 0.26] |
rise_(0.26, 0.94] |
rise_(0.94, 1.738] |
rise_(1.738, 2.938] |
rise_(2.938, 5.27] |
rise_(5.27, 10.03] |
2018-02-27 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
2018-02-26 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
2018-02-23 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
2018-02-22 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
2018-02-14 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
2018-02-13 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
2018-02-12 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
2018-02-09 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2018-02-08 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
2018-02-07 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2018-02-06 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2018-02-05 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
2018-02-02 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
2018-02-01 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2018-01-31 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
2018-01-30 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
2018-01-29 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2018-01-26 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
2018-01-25 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2018-01-24 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
2018-01-23 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
2018-01-22 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
2018-01-19 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
2018-01-18 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
2018-01-17 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
2018-01-16 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
2018-01-15 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2018-01-12 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
2018-01-11 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
2018-01-10 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
2015-04-13 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
2015-04-10 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
2015-04-09 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
2015-04-08 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
2015-04-07 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
2015-04-03 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
2015-04-02 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
2015-04-01 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
2015-03-31 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2015-03-30 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
2015-03-27 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
2015-03-26 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2015-03-25 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2015-03-24 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
2015-03-23 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
2015-03-20 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
2015-03-19 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
2015-03-18 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
2015-03-17 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
2015-03-16 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
2015-03-13 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
2015-03-12 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
2015-03-11 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2015-03-10 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
2015-03-09 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
2015-03-06 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
2015-03-05 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
2015-03-04 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
2015-03-03 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
2015-03-02 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
643 rows × 10 columns
bins=[-100,-7,-5,-3,0,3,5,7,100]
cut=pd.cut(stock['p_change'], bins)
cut.value_counts
<bound method IndexOpsMixin.value_counts of 2018-02-27 (0, 3]
2018-02-26 (3, 5]
2018-02-23 (0, 3]
2018-02-22 (0, 3]
2018-02-14 (0, 3]
2018-02-13 (0, 3]
2018-02-12 (3, 5]
2018-02-09 (-7, -5]
2018-02-08 (0, 3]
2018-02-07 (-3, 0]
2018-02-06 (-5, -3]
2018-02-05 (0, 3]
2018-02-02 (0, 3]
2018-02-01 (-7, -5]
2018-01-31 (-3, 0]
2018-01-30 (0, 3]
2018-01-29 (-3, 0]
2018-01-26 (0, 3]
2018-01-25 (-5, -3]
2018-01-24 (-3, 0]
2018-01-23 (0, 3]
2018-01-22 (-3, 0]
2018-01-19 (0, 3]
2018-01-18 (0, 3]
2018-01-17 (0, 3]
2018-01-16 (3, 5]
2018-01-15 (-5, -3]
2018-01-12 (0, 3]
2018-01-11 (-3, 0]
2018-01-10 (-3, 0]
...
2015-04-13 (7, 100]
2015-04-10 (-3, 0]
2015-04-09 (5, 7]
2015-04-08 (5, 7]
2015-04-07 (5, 7]
2015-04-03 (0, 3]
2015-04-02 (0, 3]
2015-04-01 (0, 3]
2015-03-31 (-3, 0]
2015-03-30 (3, 5]
2015-03-27 (5, 7]
2015-03-26 (-3, 0]
2015-03-25 (-3, 0]
2015-03-24 (0, 3]
2015-03-23 (0, 3]
2015-03-20 (-3, 0]
2015-03-19 (0, 3]
2015-03-18 (0, 3]
2015-03-17 (0, 3]
2015-03-16 (0, 3]
2015-03-13 (0, 3]
2015-03-12 (-3, 0]
2015-03-11 (-3, 0]
2015-03-10 (0, 3]
2015-03-09 (0, 3]
2015-03-06 (7, 100]
2015-03-05 (0, 3]
2015-03-04 (0, 3]
2015-03-03 (0, 3]
2015-03-02 (0, 3]
Name: p_change, Length: 643, dtype: category
Categories (8, interval[int64]): [(-100, -7] < (-7, -5] < (-5, -3] < (-3, 0] < (0, 3] < (3, 5] < (5, 7] < (7, 100]]>
one_hot=pd.get_dummies(qcut,prefix="涨跌幅").head()
one_hot
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|
涨跌幅_(-10.030999999999999, -4.836] |
涨跌幅_(-4.836, -2.444] |
涨跌幅_(-2.444, -1.352] |
涨跌幅_(-1.352, -0.462] |
涨跌幅_(-0.462, 0.26] |
涨跌幅_(0.26, 0.94] |
涨跌幅_(0.94, 1.738] |
涨跌幅_(1.738, 2.938] |
涨跌幅_(2.938, 5.27] |
涨跌幅_(5.27, 10.03] |
2018-02-27 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
2018-02-26 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
2018-02-23 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
2018-02-22 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
2018-02-14 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
# 数据的合并
pd.concat([stock,one_hot],axis=1).head()
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|
open |
high |
close |
low |
volume |
price_change |
p_change |
ma5 |
ma10 |
ma20 |
... |
涨跌幅_(-10.030999999999999, -4.836] |
涨跌幅_(-4.836, -2.444] |
涨跌幅_(-2.444, -1.352] |
涨跌幅_(-1.352, -0.462] |
涨跌幅_(-0.462, 0.26] |
涨跌幅_(0.26, 0.94] |
涨跌幅_(0.94, 1.738] |
涨跌幅_(1.738, 2.938] |
涨跌幅_(2.938, 5.27] |
涨跌幅_(5.27, 10.03] |
2015-03-02 |
12.25 |
12.67 |
12.52 |
12.20 |
96291.73 |
0.32 |
2.62 |
12.520 |
12.520 |
12.520 |
... |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
2015-03-03 |
12.52 |
13.06 |
12.70 |
12.52 |
139071.61 |
0.18 |
1.44 |
12.610 |
12.610 |
12.610 |
... |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
2015-03-04 |
12.80 |
12.92 |
12.90 |
12.61 |
67075.44 |
0.20 |
1.57 |
12.707 |
12.707 |
12.707 |
... |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
2015-03-05 |
12.88 |
13.45 |
13.16 |
12.87 |
93180.39 |
0.26 |
2.02 |
12.820 |
12.820 |
12.820 |
... |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
2015-03-06 |
13.17 |
14.48 |
14.28 |
13.13 |
179831.72 |
1.12 |
8.51 |
13.112 |
13.112 |
13.112 |
... |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
5 rows × 24 columns
stock
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|
open |
high |
close |
low |
volume |
price_change |
p_change |
ma5 |
ma10 |
ma20 |
v_ma5 |
v_ma10 |
v_ma20 |
turnover |
2018-02-27 |
23.53 |
25.88 |
24.16 |
23.53 |
95578.03 |
0.63 |
2.68 |
22.942 |
22.142 |
22.875 |
53782.64 |
46738.65 |
55576.11 |
2.39 |
2018-02-26 |
22.80 |
23.78 |
23.53 |
22.80 |
60985.11 |
0.69 |
3.02 |
22.406 |
21.955 |
22.942 |
40827.52 |
42736.34 |
56007.50 |
1.53 |
2018-02-23 |
22.88 |
23.37 |
22.82 |
22.71 |
52914.01 |
0.54 |
2.42 |
21.938 |
21.929 |
23.022 |
35119.58 |
41871.97 |
56372.85 |
1.32 |
2018-02-22 |
22.25 |
22.76 |
22.28 |
22.02 |
36105.01 |
0.36 |
1.64 |
21.446 |
21.909 |
23.137 |
35397.58 |
39904.78 |
60149.60 |
0.90 |
2018-02-14 |
21.49 |
21.99 |
21.92 |
21.48 |
23331.04 |
0.44 |
2.05 |
21.366 |
21.923 |
23.253 |
33590.21 |
42935.74 |
61716.11 |
0.58 |
2018-02-13 |
21.40 |
21.90 |
21.48 |
21.31 |
30802.45 |
0.28 |
1.32 |
21.342 |
22.103 |
23.387 |
39694.65 |
45518.14 |
65161.68 |
0.77 |
2018-02-12 |
20.70 |
21.40 |
21.19 |
20.63 |
32445.39 |
0.82 |
4.03 |
21.504 |
22.338 |
23.533 |
44645.16 |
45679.94 |
68686.33 |
0.81 |
2018-02-09 |
21.20 |
21.46 |
20.36 |
20.19 |
54304.01 |
-1.50 |
-6.86 |
21.920 |
22.596 |
23.645 |
48624.36 |
48982.38 |
70552.47 |
1.36 |
2018-02-08 |
21.79 |
22.09 |
21.88 |
21.75 |
27068.16 |
0.09 |
0.41 |
22.372 |
23.009 |
23.839 |
44411.98 |
48612.16 |
73852.45 |
0.68 |
2018-02-07 |
22.69 |
23.11 |
21.80 |
21.29 |
53853.25 |
-0.50 |
-2.24 |
22.480 |
23.258 |
23.929 |
52281.28 |
56315.11 |
74925.33 |
1.35 |
2018-02-06 |
22.80 |
23.55 |
22.29 |
22.20 |
55555.00 |
-0.97 |
-4.17 |
22.864 |
23.607 |
24.029 |
51341.63 |
64413.58 |
75738.95 |
1.39 |
2018-02-05 |
22.45 |
23.39 |
23.27 |
22.25 |
52341.39 |
0.65 |
2.87 |
23.172 |
23.928 |
24.112 |
46714.72 |
69278.66 |
77070.00 |
1.31 |
2018-02-02 |
22.40 |
22.70 |
22.62 |
21.53 |
33242.11 |
0.20 |
0.89 |
23.272 |
24.114 |
24.184 |
49340.40 |
70873.73 |
79929.71 |
0.83 |
2018-02-01 |
23.71 |
23.86 |
22.42 |
22.22 |
66414.64 |
-1.30 |
-5.48 |
23.646 |
24.365 |
24.279 |
52812.35 |
80394.43 |
88480.92 |
1.66 |
2018-01-31 |
23.85 |
23.98 |
23.72 |
23.31 |
49155.02 |
-0.11 |
-0.46 |
24.036 |
24.583 |
24.411 |
60348.94 |
80496.48 |
91666.75 |
1.23 |
2018-01-30 |
23.71 |
24.08 |
23.83 |
23.70 |
32420.43 |
0.05 |
0.21 |
24.350 |
24.671 |
24.365 |
77485.53 |
84805.23 |
92943.35 |
0.81 |
2018-01-29 |
24.40 |
24.63 |
23.77 |
23.72 |
65469.81 |
-0.73 |
-2.98 |
24.684 |
24.728 |
24.294 |
91842.60 |
91692.73 |
93456.22 |
1.64 |
2018-01-26 |
24.27 |
24.74 |
24.49 |
24.22 |
50601.83 |
0.11 |
0.45 |
24.956 |
24.694 |
24.221 |
92407.05 |
92122.56 |
91980.51 |
1.27 |
2018-01-25 |
24.99 |
24.99 |
24.37 |
24.23 |
104097.59 |
-0.93 |
-3.68 |
25.084 |
24.669 |
24.109 |
107976.51 |
99092.73 |
92262.67 |
2.61 |
2018-01-24 |
25.49 |
26.28 |
25.29 |
25.20 |
134838.00 |
-0.20 |
-0.79 |
25.130 |
24.599 |
23.997 |
100644.02 |
93535.55 |
89522.22 |
3.37 |
2018-01-23 |
25.15 |
25.53 |
25.50 |
24.93 |
104205.76 |
0.39 |
1.55 |
24.992 |
24.450 |
23.844 |
92124.92 |
87064.33 |
85876.80 |
2.61 |
2018-01-22 |
25.14 |
25.40 |
25.13 |
24.75 |
68292.08 |
-0.01 |
-0.04 |
24.772 |
24.296 |
23.644 |
91542.85 |
84861.33 |
84970.00 |
1.71 |
2018-01-19 |
24.60 |
25.34 |
25.13 |
24.42 |
128449.11 |
0.53 |
2.15 |
24.432 |
24.254 |
23.537 |
91838.07 |
88985.70 |
82975.10 |
3.21 |
2018-01-18 |
24.40 |
24.88 |
24.60 |
24.30 |
67435.14 |
0.01 |
0.04 |
24.254 |
24.192 |
23.441 |
90208.95 |
96567.41 |
78252.92 |
1.69 |
2018-01-17 |
24.42 |
24.92 |
24.60 |
23.80 |
92242.51 |
0.20 |
0.82 |
24.068 |
24.239 |
23.378 |
86427.08 |
102837.01 |
77049.61 |
2.31 |
2018-01-16 |
23.40 |
24.60 |
24.40 |
23.30 |
101295.42 |
0.96 |
4.10 |
23.908 |
24.058 |
23.321 |
82003.73 |
101081.47 |
74590.92 |
2.54 |
2018-01-15 |
24.01 |
24.23 |
23.43 |
23.30 |
69768.17 |
-0.80 |
-3.30 |
23.820 |
23.860 |
23.257 |
78179.81 |
95219.71 |
71006.65 |
1.75 |
2018-01-12 |
23.70 |
25.15 |
24.24 |
23.42 |
120303.53 |
0.56 |
2.37 |
24.076 |
23.748 |
23.236 |
86133.33 |
91838.46 |
69690.35 |
3.01 |
2018-01-11 |
23.67 |
23.85 |
23.67 |
23.21 |
48525.75 |
-0.12 |
-0.50 |
24.130 |
23.548 |
23.197 |
102925.87 |
85432.61 |
65928.23 |
1.21 |
2018-01-10 |
24.10 |
24.60 |
23.80 |
23.40 |
70125.79 |
-0.14 |
-0.58 |
24.410 |
23.394 |
23.204 |
119246.95 |
85508.89 |
66934.89 |
1.76 |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
2015-04-13 |
19.60 |
21.30 |
21.13 |
19.50 |
171822.69 |
1.70 |
8.75 |
19.228 |
17.812 |
16.563 |
149620.34 |
114456.84 |
111752.31 |
5.88 |
2015-04-10 |
19.55 |
19.89 |
19.43 |
19.20 |
112962.15 |
-0.19 |
-0.97 |
18.334 |
17.276 |
16.230 |
133648.38 |
109309.78 |
106228.29 |
3.87 |
2015-04-09 |
18.28 |
19.89 |
19.62 |
18.02 |
183119.05 |
1.20 |
6.51 |
17.736 |
16.826 |
15.964 |
124323.21 |
106501.34 |
104829.10 |
6.27 |
2015-04-08 |
17.60 |
18.53 |
18.42 |
17.60 |
157725.97 |
0.88 |
5.02 |
17.070 |
16.394 |
15.698 |
101421.29 |
97906.88 |
101658.57 |
5.40 |
2015-04-07 |
16.54 |
17.98 |
17.54 |
16.50 |
122471.85 |
0.88 |
5.28 |
16.620 |
16.120 |
15.510 |
86769.62 |
97473.29 |
98832.94 |
4.19 |
2015-04-03 |
16.44 |
16.77 |
16.66 |
16.25 |
91962.88 |
0.22 |
1.34 |
16.396 |
15.904 |
15.348 |
79293.34 |
94172.24 |
99956.63 |
3.15 |
2015-04-02 |
16.21 |
16.50 |
16.44 |
16.21 |
66336.32 |
0.15 |
0.92 |
16.218 |
15.772 |
15.229 |
84971.19 |
92655.96 |
104350.08 |
2.27 |
2015-04-01 |
16.18 |
16.48 |
16.29 |
16.00 |
68609.42 |
0.12 |
0.74 |
15.916 |
15.666 |
15.065 |
88679.47 |
95386.75 |
105692.28 |
2.35 |
2015-03-31 |
16.78 |
16.88 |
16.17 |
16.07 |
84467.62 |
-0.25 |
-1.52 |
15.718 |
15.568 |
14.896 |
94392.47 |
100679.68 |
105615.58 |
2.89 |
2015-03-30 |
15.99 |
16.63 |
16.42 |
15.99 |
85090.45 |
0.65 |
4.12 |
15.620 |
15.469 |
14.722 |
108176.96 |
108109.99 |
108345.78 |
2.91 |
2015-03-27 |
14.90 |
15.86 |
15.77 |
14.90 |
120352.13 |
0.84 |
5.63 |
15.412 |
15.314 |
14.527 |
109051.14 |
109047.78 |
108905.84 |
4.12 |
2015-03-26 |
15.14 |
15.35 |
14.93 |
14.91 |
84877.75 |
-0.37 |
-2.42 |
15.326 |
15.184 |
14.462 |
100340.74 |
103146.79 |
108303.41 |
2.91 |
2015-03-25 |
15.97 |
15.97 |
15.30 |
15.18 |
97174.40 |
-0.38 |
-2.42 |
15.416 |
15.102 |
14.436 |
102094.02 |
103156.85 |
109604.83 |
3.33 |
2015-03-24 |
15.38 |
16.16 |
15.68 |
15.28 |
153390.08 |
0.30 |
1.95 |
15.418 |
15.002 |
14.385 |
106966.89 |
105410.25 |
110336.03 |
5.25 |
2015-03-23 |
15.34 |
15.56 |
15.38 |
15.25 |
89461.32 |
0.04 |
0.26 |
15.318 |
14.899 |
14.304 |
108043.02 |
100192.60 |
107645.16 |
3.06 |
2015-03-20 |
15.38 |
15.48 |
15.34 |
15.18 |
76800.13 |
-0.04 |
-0.26 |
15.216 |
14.792 |
14.232 |
109044.42 |
105741.03 |
108857.41 |
2.63 |
2015-03-19 |
15.20 |
15.64 |
15.38 |
15.11 |
93644.19 |
0.07 |
0.46 |
15.042 |
14.686 |
14.153 |
105952.84 |
116044.19 |
111147.22 |
3.21 |
2015-03-18 |
15.18 |
15.66 |
15.31 |
15.02 |
121538.71 |
0.13 |
0.86 |
14.788 |
14.464 |
14.058 |
104219.67 |
115997.81 |
112493.60 |
4.16 |
2015-03-17 |
14.90 |
15.44 |
15.18 |
14.63 |
158770.77 |
0.31 |
2.08 |
14.586 |
14.223 |
13.954 |
103853.62 |
110551.48 |
111739.85 |
5.43 |
2015-03-16 |
14.52 |
15.05 |
14.87 |
14.51 |
94468.30 |
0.40 |
2.76 |
14.480 |
13.975 |
13.843 |
92342.17 |
108581.56 |
107464.31 |
3.23 |
2015-03-13 |
14.13 |
14.50 |
14.47 |
14.08 |
61342.22 |
0.36 |
2.55 |
14.368 |
13.740 |
13.740 |
102437.64 |
108763.91 |
108763.91 |
2.10 |
2015-03-12 |
14.11 |
14.80 |
14.11 |
13.95 |
84978.37 |
-0.19 |
-1.33 |
14.330 |
13.659 |
13.659 |
126135.54 |
114032.98 |
114032.98 |
2.91 |
2015-03-11 |
14.80 |
15.08 |
14.30 |
14.14 |
119708.43 |
-0.35 |
-2.39 |
14.140 |
13.603 |
13.603 |
127775.94 |
117664.81 |
117664.81 |
4.10 |
2015-03-10 |
14.20 |
14.80 |
14.65 |
14.01 |
101213.51 |
0.34 |
2.38 |
13.860 |
13.503 |
13.503 |
117249.34 |
117372.87 |
117372.87 |
3.46 |
2015-03-09 |
14.14 |
14.85 |
14.31 |
13.80 |
144945.66 |
0.03 |
0.21 |
13.470 |
13.312 |
13.312 |
124820.96 |
120066.09 |
120066.09 |
4.96 |
2015-03-06 |
13.17 |
14.48 |
14.28 |
13.13 |
179831.72 |
1.12 |
8.51 |
13.112 |
13.112 |
13.112 |
115090.18 |
115090.18 |
115090.18 |
6.16 |
2015-03-05 |
12.88 |
13.45 |
13.16 |
12.87 |
93180.39 |
0.26 |
2.02 |
12.820 |
12.820 |
12.820 |
98904.79 |
98904.79 |
98904.79 |
3.19 |
2015-03-04 |
12.80 |
12.92 |
12.90 |
12.61 |
67075.44 |
0.20 |
1.57 |
12.707 |
12.707 |
12.707 |
100812.93 |
100812.93 |
100812.93 |
2.30 |
2015-03-03 |
12.52 |
13.06 |
12.70 |
12.52 |
139071.61 |
0.18 |
1.44 |
12.610 |
12.610 |
12.610 |
117681.67 |
117681.67 |
117681.67 |
4.76 |
2015-03-02 |
12.25 |
12.67 |
12.52 |
12.20 |
96291.73 |
0.32 |
2.62 |
12.520 |
12.520 |
12.520 |
96291.73 |
96291.73 |
96291.73 |
3.30 |
643 rows × 14 columns
# 1、先根据对应的日期找到星期几
date = pd.to_datetime(stock.index).weekday
stock['week'] = date
# 2、把p_change按照大小分类,以0为界限
stock['posi_neg'] = np.where(stock['p_change'] > 0, 1, 0)
# 通过交叉表找寻两列数据的关系
data = pd.crosstab(stock['week'], stock['posi_neg'])
data
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posi_neg |
0 |
1 |
week |
|
|
--- |
--- |
--- |
0 |
63 |
62 |
1 |
55 |
76 |
2 |
61 |
71 |
3 |
63 |
65 |
4 |
59 |
68 |
pona_sum=data.sum(axis=1)
pona_sum
week
0 125
1 131
2 132
3 128
4 127
dtype: int64
data=data.div(pona_sum,axis=0)
data
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posi_neg |
0 |
1 |
week |
|
|
--- |
--- |
--- |
0 |
0.504000 |
0.496000 |
1 |
0.419847 |
0.580153 |
2 |
0.462121 |
0.537879 |
3 |
0.492188 |
0.507812 |
4 |
0.464567 |
0.535433 |
data.plot(kind="bar",stacked=True)
<matplotlib.axes._subplots.AxesSubplot at 0x7f8cd40efeb8>
# 透视表实现更加简单
stock.pivot_table(["posi_neg"],index=["week"])
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|
posi_neg |
week |
|
--- |
--- |
0 |
0.496000 |
1 |
0.580153 |
2 |
0.537879 |
3 |
0.507812 |
4 |
0.535433 |
# 分组
col =pd.DataFrame({'color': ['white','red','green','red','green'], 'object': ['pen','pencil','pencil','ashtray','pen'],'price1':[5.56,4.20,1.30,0.56,2.75],'price2':[4.75,4.12,1.60,0.75,3.15]})
col
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|
color |
object |
price1 |
price2 |
0 |
white |
pen |
5.56 |
4.75 |
1 |
red |
pencil |
4.20 |
4.12 |
2 |
green |
pencil |
1.30 |
1.60 |
3 |
red |
ashtray |
0.56 |
0.75 |
4 |
green |
pen |
2.75 |
3.15 |
col.groupby(['color'])['price1'].mean()
color
green 2.025
red 2.380
white 5.560
Name: price1, dtype: float64
col.groupby(['color'], as_index=False)['price1'].mean()
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|
color |
price1 |
0 |
green |
2.025 |
1 |
red |
2.380 |
2 |
white |
5.560 |
综合示例
movie=pd.read_csv("/home/python/练习/IMDB/IMDB-Movie-Data.csv")
movie.head()
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|
Rank |
Title |
Genre |
Description |
Director |
Actors |
Year |
Runtime (Minutes) |
Rating |
Votes |
Revenue (Millions) |
Metascore |
0 |
1 |
Guardians of the Galaxy |
Action,Adventure,Sci-Fi |
A group of intergalactic criminals are forced ... |
James Gunn |
Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S... |
2014 |
121 |
8.1 |
757074 |
333.13 |
76.0 |
1 |
2 |
Prometheus |
Adventure,Mystery,Sci-Fi |
Following clues to the origin of mankind, a te... |
Ridley Scott |
Noomi Rapace, Logan Marshall-Green, Michael Fa... |
2012 |
124 |
7.0 |
485820 |
126.46 |
65.0 |
2 |
3 |
Split |
Horror,Thriller |
Three girls are kidnapped by a man with a diag... |
M. Night Shyamalan |
James McAvoy, Anya Taylor-Joy, Haley Lu Richar... |
2016 |
117 |
7.3 |
157606 |
138.12 |
62.0 |
3 |
4 |
Sing |
Animation,Comedy,Family |
In a city of humanoid animals, a hustling thea... |
Christophe Lourdelet |
Matthew McConaughey,Reese Witherspoon, Seth Ma... |
2016 |
108 |
7.2 |
60545 |
270.32 |
59.0 |
4 |
5 |
Suicide Squad |
Action,Adventure,Fantasy |
A secret government agency recruits some of th... |
David Ayer |
Will Smith, Jared Leto, Margot Robbie, Viola D... |
2016 |
123 |
6.2 |
393727 |
325.02 |
40.0 |
# 想知道这些电影数据中评分的平均分,导演的人数等信息,我们应该怎么获取?
mean=movie["Rating"].mean()
print("电影平均分为{}".format(mean))
电影平均分为6.723199999999999
director_count=movie["Director"].unique().size
print("共有{}名导演".format(director_count))
共有644名导演
# 2 对于这一组电影数据,如果我们想看Rating,Runtime (Minutes)的分布情况,应该如何呈现数据?
movie["Rating"].plot(kind="hist",figsize=(20,8),bins=20)
<matplotlib.axes._subplots.AxesSubplot at 0x7f8cd1b40b38>
import matplotlib.pyplot as plt
plt.figure(figsize=(20,8),dpi=80)
plt.hist(movie["Rating"].values,bins=20)
#求出最大最小值
max_ = movie["Rating"].max()
min_ = movie["Rating"].min()
# 生成刻度列表
t1 = np.linspace(min_,max_,num=21)
# [ 1.9 2.255 2.61 2.965 3.32 3.675 4.03 4.385 4.74 5.095 5.45 5.805 6.16 6.515 6.87 7.225 7.58 7.935 8.29 8.645 9\. ]
# 修改刻度
plt.xticks(np.linspace(min_,max_,num=21))
# 添加网格
plt.grid()
plt.show()
# 3 对于这一组电影数据,如果我们希望统计电影分类(genre)的情况,应该如何处理数据?
movie.head()
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|
Rank |
Title |
Genre |
Description |
Director |
Actors |
Year |
Runtime (Minutes) |
Rating |
Votes |
Revenue (Millions) |
Metascore |
0 |
1 |
Guardians of the Galaxy |
Action,Adventure,Sci-Fi |
A group of intergalactic criminals are forced ... |
James Gunn |
Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S... |
2014 |
121 |
8.1 |
757074 |
333.13 |
76.0 |
1 |
2 |
Prometheus |
Adventure,Mystery,Sci-Fi |
Following clues to the origin of mankind, a te... |
Ridley Scott |
Noomi Rapace, Logan Marshall-Green, Michael Fa... |
2012 |
124 |
7.0 |
485820 |
126.46 |
65.0 |
2 |
3 |
Split |
Horror,Thriller |
Three girls are kidnapped by a man with a diag... |
M. Night Shyamalan |
James McAvoy, Anya Taylor-Joy, Haley Lu Richar... |
2016 |
117 |
7.3 |
157606 |
138.12 |
62.0 |
3 |
4 |
Sing |
Animation,Comedy,Family |
In a city of humanoid animals, a hustling thea... |
Christophe Lourdelet |
Matthew McConaughey,Reese Witherspoon, Seth Ma... |
2016 |
108 |
7.2 |
60545 |
270.32 |
59.0 |
4 |
5 |
Suicide Squad |
Action,Adventure,Fantasy |
A secret government agency recruits some of th... |
David Ayer |
Will Smith, Jared Leto, Margot Robbie, Viola D... |
2016 |
123 |
6.2 |
393727 |
325.02 |
40.0 |
# 先获取类别的清单
class_list=[i.split(",")for i in movie["Genre"]]
class_list
[['Action', 'Adventure', 'Sci-Fi'],
['Adventure', 'Mystery', 'Sci-Fi'],
['Horror', 'Thriller'],
['Animation', 'Comedy', 'Family'],
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['Drama', 'Romance'],
['Action', 'Comedy', 'Fantasy'],
['Drama', 'Horror', 'Musical'],
['Crime', 'Drama', 'Mystery'],
['Horror', 'Mystery', 'Thriller'],
['Comedy', 'Music'],
['Drama'],
['Biography', 'Crime', 'Drama'],
['Drama'],
['Action', 'Adventure', 'Comedy'],
['Crime', 'Drama', 'Mystery'],
['Drama'],
['Action', 'Comedy', 'Crime'],
['Comedy', 'Drama', 'Romance'],
['Crime', 'Drama', 'Mystery'],
['Action', 'Comedy', 'Crime'],
['Drama'],
['Drama', 'Romance'],
['Crime', 'Drama', 'Mystery'],
['Adventure', 'Comedy', 'Romance'],
['Comedy', 'Crime', 'Drama'],
['Adventure', 'Drama', 'Thriller'],
['Biography', 'Crime', 'Drama'],
['Crime', 'Drama', 'Thriller'],
['Drama', 'History', 'Thriller'],
['Action', 'Adventure', 'Sci-Fi'],
['Action', 'Comedy'],
['Horror'],
['Action', 'Crime', 'Mystery'],
['Comedy', 'Romance'],
['Comedy'],
['Action', 'Drama', 'Thriller'],
['Action', 'Adventure', 'Sci-Fi'],
['Drama', 'Mystery', 'Thriller'],
['Comedy', 'Drama', 'Romance'],
['Action', 'Fantasy', 'Horror'],
['Drama', 'Romance'],
['Biography', 'Drama'],
['Biography', 'Drama'],
['Action', 'Adventure', 'Sci-Fi'],
['Animation', 'Adventure', 'Comedy'],
['Drama', 'Mystery', 'Thriller'],
['Action', 'Horror', 'Sci-Fi'],
['Drama', 'Romance'],
['Biography', 'Drama'],
['Action', 'Adventure', 'Drama'],
['Adventure', 'Drama', 'Fantasy'],
['Drama', 'Family'],
['Comedy', 'Drama', 'Romance'],
['Drama', 'Romance', 'Sci-Fi'],
['Action', 'Adventure', 'Thriller'],
['Comedy', 'Romance'],
['Crime', 'Drama', 'Horror'],
['Comedy', 'Fantasy'],
['Action', 'Comedy', 'Crime'],
['Adventure', 'Drama', 'Romance'],
['Action', 'Crime', 'Drama'],
['Crime', 'Horror', 'Thriller'],
['Romance', 'Sci-Fi', 'Thriller'],
['Comedy', 'Drama', 'Romance'],
['Crime', 'Drama'],
['Crime', 'Drama', 'Mystery'],
['Action', 'Adventure', 'Sci-Fi'],
['Animation', 'Fantasy'],
['Animation', 'Adventure', 'Comedy'],
['Drama', 'Mystery', 'War'],
['Comedy', 'Romance'],
['Animation', 'Comedy', 'Family'],
['Comedy'],
['Horror', 'Mystery', 'Thriller'],
['Action', 'Adventure', 'Drama'],
['Comedy'],
['Drama'],
['Adventure', 'Biography', 'Drama'],
['Comedy'],
['Horror', 'Thriller'],
['Action', 'Drama', 'Family'],
['Comedy', 'Fantasy', 'Horror'],
['Comedy', 'Romance'],
['Drama', 'Mystery', 'Romance'],
['Action', 'Adventure', 'Comedy'],
['Thriller'],
['Comedy'],
['Adventure', 'Comedy', 'Sci-Fi'],
['Comedy', 'Drama', 'Fantasy'],
['Mystery', 'Thriller'],
['Comedy', 'Drama'],
['Adventure', 'Drama', 'Family'],
['Horror', 'Thriller'],
['Action', 'Drama', 'Romance'],
['Drama', 'Romance'],
['Action', 'Adventure', 'Fantasy'],
['Comedy'],
['Action', 'Biography', 'Drama'],
['Drama', 'Mystery', 'Romance'],
['Adventure', 'Drama', 'Western'],
['Drama', 'Music', 'Romance'],
['Comedy', 'Romance', 'Western'],
['Thriller'],
['Comedy', 'Drama', 'Romance'],
['Horror', 'Thriller'],
['Adventure', 'Family', 'Fantasy'],
['Crime', 'Drama', 'Mystery'],
['Horror', 'Mystery'],
['Comedy', 'Crime', 'Drama'],
['Action', 'Comedy', 'Romance'],
['Biography', 'Drama', 'History'],
['Adventure', 'Drama'],
['Drama', 'Thriller'],
['Drama'],
['Action', 'Adventure', 'Fantasy'],
['Action', 'Biography', 'Drama'],
['Drama', 'Music'],
['Comedy', 'Drama'],
['Drama', 'Thriller', 'War'],
['Action', 'Mystery', 'Thriller'],
['Horror', 'Sci-Fi', 'Thriller'],
['Comedy', 'Drama', 'Romance'],
['Action', 'Sci-Fi'],
['Action', 'Adventure', 'Fantasy'],
['Drama', 'Mystery', 'Romance'],
['Drama'],
['Action', 'Adventure', 'Thriller'],
['Action', 'Crime', 'Thriller'],
['Animation', 'Action', 'Adventure'],
['Drama', 'Fantasy', 'Mystery'],
['Drama', 'Sci-Fi'],
['Animation', 'Adventure', 'Comedy'],
['Horror', 'Thriller'],
['Action', 'Thriller'],
['Comedy'],
['Biography', 'Drama'],
['Action', 'Mystery', 'Thriller'],
['Action', 'Mystery', 'Sci-Fi'],
['Crime', 'Drama', 'Thriller'],
['Comedy', 'Romance'],
['Comedy', 'Drama', 'Romance'],
['Biography', 'Drama', 'Thriller'],
['Drama'],
['Action', 'Adventure', 'Family'],
['Animation', 'Comedy', 'Family'],
['Action', 'Crime', 'Drama'],
['Comedy'],
['Comedy', 'Crime', 'Thriller'],
['Comedy', 'Romance'],
['Animation', 'Comedy', 'Drama'],
['Action', 'Crime', 'Thriller'],
['Comedy', 'Romance'],
['Adventure', 'Biography', 'Drama'],
['Animation', 'Adventure', 'Comedy'],
['Crime', 'Drama', 'Mystery'],
['Action', 'Comedy', 'Sci-Fi'],
['Comedy', 'Fantasy', 'Horror'],
['Comedy', 'Crime'],
['Animation', 'Action', 'Adventure'],
['Action', 'Drama', 'Thriller'],
['Fantasy', 'Horror'],
['Crime', 'Drama', 'Thriller'],
['Action', 'Adventure', 'Fantasy'],
['Comedy', 'Drama', 'Romance'],
['Biography', 'Drama', 'Romance'],
['Action', 'Drama', 'History'],
['Action', 'Adventure', 'Comedy'],
['Horror', 'Thriller'],
['Horror', 'Mystery', 'Thriller'],
['Comedy', 'Romance'],
['Animation', 'Adventure', 'Comedy'],
['Crime', 'Drama', 'Mystery'],
['Crime', 'Drama', 'Mystery'],
['Adventure', 'Biography', 'Drama'],
['Horror', 'Mystery', 'Thriller'],
['Horror', 'Thriller'],
['Drama', 'Romance', 'War'],
['Adventure', 'Fantasy', 'Mystery'],
['Action', 'Adventure', 'Sci-Fi'],
['Biography', 'Drama'],
['Drama', 'Thriller'],
['Horror', 'Thriller'],
['Drama', 'Horror', 'Thriller'],
['Action', 'Adventure', 'Fantasy'],
['Action', 'Horror', 'Thriller'],
['Comedy'],
['Drama', 'Sport'],
['Comedy', 'Family'],
['Drama', 'Romance'],
['Action', 'Adventure', 'Comedy'],
['Comedy'],
['Mystery', 'Romance', 'Thriller'],
['Crime', 'Drama'],
['Action', 'Comedy'],
['Crime', 'Drama', 'Mystery'],
['Biography', 'Drama', 'Romance'],
['Comedy', 'Crime'],
['Drama', 'Thriller'],
['Drama'],
['Animation', 'Adventure', 'Comedy'],
['Action', 'Thriller'],
['Drama', 'Thriller'],
['Animation', 'Adventure', 'Comedy'],
['Crime', 'Drama', 'Mystery'],
['Thriller'],
['Biography', 'Drama', 'Sport'],
['Crime', 'Drama', 'Thriller'],
['Drama', 'Music'],
['Crime', 'Drama', 'Thriller'],
['Drama', 'Romance'],
['Animation', 'Action', 'Adventure'],
['Comedy', 'Drama'],
['Action', 'Adventure', 'Drama'],
['Biography', 'Crime', 'Drama'],
['Horror'],
['Biography', 'Drama', 'Mystery'],
['Drama', 'Romance'],
['Animation', 'Drama', 'Romance'],
['Comedy', 'Family'],
['Drama'],
['Mystery', 'Thriller'],
['Drama', 'Fantasy', 'Horror'],
['Drama', 'Romance'],
['Biography', 'Drama', 'History'],
['Comedy', 'Family'],
['Action', 'Adventure', 'Thriller'],
['Comedy', 'Drama'],
['Action', 'Adventure', 'Fantasy'],
['Action', 'Thriller'],
['Drama', 'Romance'],
['Comedy', 'Drama', 'Romance'],
['Drama', 'Horror', 'Sci-Fi'],
['Comedy', 'Horror', 'Romance'],
['Drama'],
['Action', 'Adventure', 'Sci-Fi'],
['Action', 'Adventure', 'Fantasy'],
['Action', 'Adventure', 'Drama'],
['Biography', 'Comedy', 'Drama'],
['Drama', 'Mystery', 'Romance'],
['Animation', 'Adventure', 'Comedy'],
['Drama', 'Romance', 'Sci-Fi'],
['Drama'],
['Drama', 'Fantasy'],
['Drama', 'Romance'],
['Comedy', 'Horror', 'Thriller'],
['Comedy', 'Drama', 'Romance'],
['Crime', 'Drama'],
['Comedy', 'Romance'],
['Action', 'Drama', 'Family'],
['Comedy', 'Drama', 'Romance'],
['Action', 'Thriller', 'War'],
['Action', 'Comedy', 'Horror'],
['Biography', 'Drama', 'Sport'],
['Adventure', 'Comedy', 'Drama'],
['Comedy', 'Romance'],
['Comedy', 'Romance'],
['Comedy', 'Drama', 'Romance'],
['Action', 'Adventure', 'Crime'],
['Comedy', 'Romance'],
['Animation', 'Action', 'Adventure'],
['Action', 'Crime', 'Sci-Fi'],
['Drama'],
['Comedy', 'Drama', 'Romance'],
['Crime', 'Thriller'],
['Comedy', 'Horror', 'Sci-Fi'],
['Drama', 'Thriller'],
['Drama', 'Fantasy', 'Horror'],
['Thriller'],
['Adventure', 'Drama', 'Family'],
['Mystery', 'Sci-Fi', 'Thriller'],
['Biography', 'Crime', 'Drama'],
['Drama', 'Fantasy', 'Horror'],
['Action', 'Adventure', 'Thriller'],
['Crime', 'Drama', 'Horror'],
['Crime', 'Drama', 'Fantasy'],
['Adventure', 'Family', 'Fantasy'],
['Action', 'Adventure', 'Drama'],
['Action', 'Comedy', 'Horror'],
['Comedy', 'Drama', 'Family'],
['Action', 'Thriller'],
['Action', 'Adventure', 'Sci-Fi'],
['Adventure', 'Drama', 'Fantasy'],
['Drama'],
['Drama'],
['Comedy'],
['Drama'],
['Comedy', 'Drama', 'Music'],
['Drama', 'Fantasy', 'Music'],
['Drama'],
['Thriller'],
['Comedy', 'Horror'],
['Action', 'Comedy', 'Sport'],
['Horror'],
['Comedy', 'Drama'],
['Action', 'Drama', 'Thriller'],
['Drama', 'Romance'],
['Horror', 'Mystery'],
['Adventure', 'Drama', 'Fantasy'],
['Thriller'],
['Comedy', 'Romance'],
['Action', 'Sci-Fi', 'Thriller'],
['Fantasy', 'Mystery', 'Thriller'],
['Biography', 'Drama'],
['Crime', 'Drama'],
['Action', 'Adventure', 'Sci-Fi'],
['Adventure'],
['Comedy', 'Drama'],
['Comedy', 'Drama'],
['Comedy', 'Drama', 'Romance'],
['Adventure', 'Comedy', 'Drama'],
['Action', 'Sci-Fi', 'Thriller'],
['Comedy', 'Romance'],
['Action', 'Fantasy', 'Horror'],
['Crime', 'Drama', 'Thriller'],
['Action', 'Drama', 'Thriller'],
['Crime', 'Drama', 'Mystery'],
['Crime', 'Drama', 'Mystery'],
['Drama', 'Sci-Fi', 'Thriller'],
['Biography', 'Drama', 'History'],
['Crime', 'Horror', 'Thriller'],
['Drama'],
['Drama', 'Mystery', 'Thriller'],
['Adventure', 'Biography'],
['Adventure', 'Biography', 'Crime'],
['Action', 'Horror', 'Thriller'],
['Action', 'Adventure', 'Western'],
['Horror', 'Thriller'],
['Drama', 'Mystery', 'Thriller'],
['Comedy', 'Drama', 'Musical'],
['Horror', 'Mystery'],
['Biography', 'Drama', 'Sport'],
['Comedy', 'Family', 'Romance'],
['Drama', 'Mystery', 'Thriller'],
['Comedy'],
['Drama'],
['Drama', 'Thriller'],
['Biography', 'Drama', 'Family'],
['Comedy', 'Drama', 'Family'],
['Drama', 'Fantasy', 'Musical'],
['Comedy'],
['Adventure', 'Family'],
['Adventure', 'Comedy', 'Fantasy'],
['Horror', 'Thriller'],
['Drama', 'Romance'],
['Horror'],
['Biography', 'Drama', 'History'],
['Action', 'Adventure', 'Fantasy'],
['Drama', 'Family', 'Music'],
['Comedy', 'Drama', 'Romance'],
['Action', 'Adventure', 'Horror'],
['Comedy'],
['Crime', 'Drama', 'Mystery'],
['Horror'],
['Drama', 'Music', 'Romance'],
['Adventure', 'Comedy'],
['Comedy', 'Family', 'Fantasy']]
genre_list=[j for i in class_list for j in i]
# 去重
genre=np.unique(genre_list)
genre
array(['Action', 'Adventure', 'Animation', 'Biography', 'Comedy', 'Crime',
'Drama', 'Family', 'Fantasy', 'History', 'Horror', 'Music',
'Musical', 'Mystery', 'Romance', 'Sci-Fi', 'Sport', 'Thriller',
'War', 'Western'], dtype='<U9')
# 创建一个用于统计电影类别的空的dataframe
genre_type=pd.DataFrame(np.zeros((1000,len(genre)),dtype=np.int32), columns=genre)
genre_type.head()
.dataframe thead th {
text-align: left;
}
.dataframe tbody tr th {
vertical-align: top;
}
|
Action |
Adventure |
Animation |
Biography |
Comedy |
Crime |
Drama |
Family |
Fantasy |
History |
Horror |
Music |
Musical |
Mystery |
Romance |
Sci-Fi |
Sport |
Thriller |
War |
Western |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
3 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
4 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
for i in range(1000):
genre_type.loc[i,class_list[i]]=1
genre_type.head()
.dataframe thead th {
text-align: left;
}
.dataframe tbody tr th {
vertical-align: top;
}
|
Action |
Adventure |
Animation |
Biography |
Comedy |
Crime |
Drama |
Family |
Fantasy |
History |
Horror |
Music |
Musical |
Mystery |
Romance |
Sci-Fi |
Sport |
Thriller |
War |
Western |
0 |
1 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
3 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
4 |
1 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
genre_type.sum(axis=0).sort_values(ascending=False). plot(kind="bar",figsize=(20,8),fontsize=30,colormap="cool")
<matplotlib.axes._subplots.AxesSubplot at 0x7f8cd01176a0>
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